With increasing desires of users to search for various digital contents, products or social issues, many web sites providing search functions, such as search portals, are providing semantic search in order to allow users to search for desired contents more accurately.
In conventional ways to provide semantic search, in general, contents are searched for based on factual information such as tags and metadata provided by content providers. For example, digital contents like videos can be searched based on descriptors directly extracted from the contents via scene analysis, line extraction, sound and voice classification, and so forth.
In such conventional methods of contents-based semantic search, however, it may be very difficult to model knowledge in a certain domain sufficiently. Furthermore, since the contents are searched for merely based on the factual descriptors created by the content providers or based on the limited descriptors extracted from the contents themselves, the likelihood is high that contents that are not intended by the users are retrieved.
That is, the conventional methods of implementing the content-based semantic search service have a drawback that a limited and inaccurate search result may be provided in response to a user's search request due to the lack of information data to be used for the search for the desired contents.
Further, recently, as SNS (Social Network Service) is widely used, electronic documents containing a wide variety of opinions about various objects including digital contents, products and social issues are being accumulated more and more. However, No method so far can provide a search that makes sufficient use of those various opinions.